Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization

• URL: http://arxiv.org/abs/2405.02816v1
• Date: Sun, 5 May 2024 05:42:33 GMT
• Title: Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization
• Authors: Hamed Zamani, Michael Bendersky,
• Abstract summary: RAG is a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models. We employ straight-through Gumbel-top-k that provides a differentiable approximation for sampling without replacement.
• Score: 35.74911182120259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Stochastic RAG--a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work. Stochastic RAG casts the retrieval process in RAG as a stochastic sampling without replacement process. Through this formulation, we employ straight-through Gumbel-top-k that provides a differentiable approximation for sampling without replacement and enables effective end-to-end optimization for RAG. We conduct extensive experiments on seven diverse datasets on a wide range of tasks, from open-domain question answering to fact verification to slot-filling for relation extraction and to dialogue systems. By applying this optimization method to a recent and effective RAG model, we advance state-of-the-art results on six out of seven datasets.

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